ArticleLipids in health and disease2025
Obesity-depression-metabolism triad: amplification of cardiometabolic multimorbidity risk in Chinese adults (CHARLS 2011-2018).
Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Comparison of the TyG index, TyG-traditional obesity indices, and TyG-novel obesity indices: does increased complexity help in predicting cardiometabolic multimorbidity?Lipids in health and disease · 2025Article
- The impact of sensory impairment on cardiometabolic multimorbidity in middle-aged and older adults: the mediating role of depressive symptoms.European journal of medical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundPrevious studies have shown that obesity and metabolic and psychological factors are related to cardiometabolic comorbidities, and this study focuses on the BRI, TyG index and depression and explores the independent and combined effects of these three factors on cardiometabolic comorbidities from multiple perspectives.
methodsWe analyzed data from 5,199 participants (baseline 2011) in the China Health and Retirement Longitudinal Study (CHARLS). The participants were stratified into four groups on the basis of the median TyG index (8.62) and BRI (4.03). Logistic regression models were used to assess associations between TyG-BRI strata, depression (CES-D score ≥ 10), and incident CMM (cardiometabolic multimorbidity), incorporating multiplicative interaction terms (BRI×TyG×Depression) and additive interaction indices (RERI/AP). A composite score was developed using standardized biomarker z scores and depression status, with predicti·ve performance evaluated via ROC-AUC comparisons (DeLong’s test) and restricted cubic splines.
resultsDuring the 7-year follow-up period, 331 incident CMM cases were documented. Machine learning models (random forest and XGBoost) identified BRI and TyG as the top predictors of CMM. Logistic regression revealed the highest risk in the high-BRI & high-TyG group (OR = 3.89, 95% CI = 3.02–5.01 vs. low-BRI and low-TyG). Tripartite exposure (high BRI and high TyG and depression) was associated with a striking 14-fold increased risk (OR = 14.2, 95% CI = 7.95–27.6), with a significant multiplicative interaction (p = 0.016). The composite cardiometabolic‒psychosocial score showed superior predictive capacity (AUC = 0.70 vs. BRI = 0.66, TyG = 0.65, depression = 0.56; DeLong’s p < 0.001) and dose‒responsive risk escalation (P-overall < 0.001, P for nonlinearity = 0.70).
conclusionThis study identifies BRI, TyG, and depression as independent and synergistic predictors of CMM. Our findings advocate integrated clinical strategies for effective CMM prevention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.